Hello World

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Prerequisites

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1. One-shot prompt

digraph Hello {
    graph [goal="Write a haiku about software workflows"]
    rankdir=LR

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare, label="Exit"]

    compose [label="Compose", prompt="Write a haiku (5-7-5 syllable) about software workflows. Output only the haiku, nothing else.", shape=tab, reasoning_effort="low"]

    start -> compose -> exit
}

fabro run docs/internal/demo/01-hello.fabro

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What’s happening

• shape=tab makes this a prompt node — a single LLM call with no tool access. Good for generation, summarization, and classification.
• reasoning_effort="low" tells the model to think less. This is a simple task that doesn’t need deep reasoning.
• graph [goal="..."] describes the workflow’s purpose. Fabro uses it in preambles and agent context.

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2. Agent with tools

digraph ToolUse {
    graph [goal="Explore the current directory using shell tools"]
    rankdir=LR

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare, label="Exit"]

    explore [label="Explore", prompt="Use bash to list the files in the current directory, then read the first 5 lines of any README or CLAUDE.md file you find. Summarize what this project is about in 2-3 sentences."]

    start -> explore -> exit
}

fabro run docs/internal/demo/02-tool-use.fabro

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What’s happening

• No shape attribute means the default box — an agent node.
• The agent has access to built-in tools: shell, read_file, write_file, edit_file, grep, glob, web_search, and web_fetch.
• The agent decides which tools to call and when to stop. Fabro handles the tool loop automatically.

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Prompt vs. agent nodes

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3. Sub-agents

digraph SubAgent {
    graph [goal="Research and summarize using a sub-agent"]
    rankdir=LR

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare, label="Exit"]

    research [label="Research", prompt="You have a sub-agent available via the spawn_agent tool. Spawn a sub-agent to list the files in the current directory and read the first 10 lines of any README or CLAUDE.md. Then, using the sub-agent's findings, write a 2-sentence summary of the project."]

    start -> research -> exit
}

fabro run docs/internal/demo/03-subagent.fabro

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What’s happening

• The parent agent uses spawn_agent to create a child session, then wait to collect the result.
• Sub-agents have their own tool access and conversation history — they don’t see the parent’s context.
• This pattern is useful for parallelizing research, isolating risky operations, or keeping the parent’s context window lean.

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What you’ve learned

• Prompt nodes (shape=tab) make a single LLM call — no tools
• Agent nodes (default box) run a multi-turn tool loop
• Sub-agents let an agent delegate to independent child sessions
• Every workflow needs a start node, an exit node, and a goal

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